By the fastCRW team · Benchmarks/pricing verified 2026-05-18 · fastCRW launch pricing expires 2026-06-01 · Verify independently before buying.
Disclosure: We build fastCRW, so this buyer's guide is vendor-authored — weight it accordingly. We have kept the places where other tools genuinely win explicit, and we publish our worst benchmark number alongside our best, because a guide that hides the tail is not useful to you.
What makes a web data API "LLM-ready"
An LLM-ready web data API is not just a scraper with a JSON envelope. The phrase means the output drops into a retrieval or agent pipeline without a hand-built cleanup stage. Three properties decide whether an API earns the label:
- Clean markdown that preserves structure. Headings, lists, tables, and link text survive; navigation chrome, cookie banners, and footers are stripped. Markdown costs far fewer tokens than raw HTML and chunks predictably for embeddings.
- Structured JSON via schema. When you need fields, not prose — price, author, SKU, publish date — the API should accept a JSON schema and return typed values, not a wall of text you parse downstream.
- Freshness and search-then-scrape. Agents reasoning about the live web need a way to discover URLs (search) and fetch their content in the same loop, not a stale crawl from last week.
Tools that emit raw HTML or a brittle DOM tree are not LLM-ready in this sense; they are an upstream dependency you still have to finish. The differentiator is whether the markdown and JSON are accurate and complete, because garbage in means garbage RAG.
The buyer's criteria
If you are choosing an API to feed clean web data into RAG or agents, rank candidates on three measurable axes — in this order.
Extraction accuracy (recall on labeled data)
This is the criterion most buyer's guides skip because it is hard to measure, and it is the one that decides downstream quality. If the API silently drops half a page's content, your retriever never sees it. The only honest way to compare is recall against a labeled dataset, not a vendor's hand-picked demo URL.
Latency: median and the tail
A single "average latency" number hides the story. What matters is the median (your typical request) and the tail (p90/p99), because the slow tail is what times out an agent mid-reasoning. Insist on the full split; treat any vendor that quotes one mean as withholding information.
Pricing model and self-host option
Per-page flat pricing is predictable; per-GB or per-feature metering balloons unpredictably at agent scale. And an API you can self-host gives you a hard worst-case cost ceiling — the server bill — that a hosted-only model structurally cannot offer.
LLM-ready web data APIs compared
The market splits into three rough camps. Here is how the main options map, with the trade-off each one asks you to accept.
| Tool | Camp | LLM-ready output | Self-host | Trade-off to accept |
|---|---|---|---|---|
| fastCRW | Open-core scrape + crawl + search | Markdown + JSON schema + search | Yes (AGPL-3.0) | Worst p90 of the three benched; no built-in anti-bot |
| Firecrawl | Managed AI web-data API | Markdown + JSON + agentic endpoints | AGPL, heavy stack | Cloud-only for full feature set; extract often billed separately |
| Tavily / Exa | Search-first for agents | Search results + snippets | No | Search-native, not a full-page scrape/crawl engine |
| Jina Reader (r.jina.ai) | URL-to-markdown | Thin markdown | No (token-metered) | One URL at a time; no crawl, no schema extraction |
If you want a deeper field comparison of full scrape engines, our best web scraping APIs roundup and best web scraping API for 2026 guide go tool by tool. This page is the LLM-readiness lens specifically.
fastCRW: accuracy-led, with honest tail disclosure
fastCRW is an open-core, Firecrawl-compatible engine — a single static Rust binary, AGPL-3.0, drop-in after a base-URL swap. On the criteria above, here is exactly where it lands, good number and bad number together.
Highest truth-recall of the three tools tested
On Firecrawl's own public scrape-content-dataset-v1 — 819 of its 1,000 URLs carry labeled ground truth — fastCRW posted the highest truth-recall of the three tools tested: 63.74% of 819 labeled URLs, versus Crawl4AI 59.95% and Firecrawl 56.04% (diagnose_3way.py, 2026-05-08). For an LLM-ready API, recall is the headline criterion, because content the scraper drops is content your retriever can never surface.
p50 beats Firecrawl; p90 is the worst of three (disclosed)
On latency, fastCRW's median is p50 1914 ms, beating Firecrawl's 2305 ms and effectively tied with Crawl4AI (1916 ms). But its p90 is 14157 ms — the worst of the three (Crawl4AI 4754 ms, Firecrawl 6937 ms). We will not hide that. It is causal, not incidental: the chrome-stealth fallback that recovers the URLs the other tools miss — the same mechanism behind the recall lead — is what produces the slow tail. You get higher recall by paying for it on a fraction of hard URLs. Scrape-success was 87.7% (877 of 1,000) with 0 thrown errors across 3,000 requests in the same run. Always read the full p50/p90/p99 split, never a single mean.
1 credit = 1 page; self-host for $0
Pricing is flat: a scrape is 1 credit (http/lightpanda renderer), 2 credits when chrome-rendered, and JSON-schema extraction is 5 credits — folded into the per-page meter, not a separate token subscription. Self-hosting the AGPL-3.0 engine costs $0 per 1,000 scrapes; you pay only for your own server, versus roughly $0.83–5.33 per 1,000 on Firecrawl's hosted tiers (competitor-prices.lock.md, verified 2026-05-18). See live tiers on /pricing rather than trusting a hard-coded table.
Where the others genuinely win
An honest buyer's guide has to name these plainly:
- Firecrawl on the tail and the feature surface. Its p90 (6937 ms) is less than half of fastCRW's, and it ships agentic and deep-research endpoints fastCRW does not have. If your workload is tail-latency-sensitive or depends on those endpoints, Firecrawl is the right call.
- Tavily and Exa on search-first agents. If your primary need is live web search inside an agent loop with answer synthesis, a search-native API is purpose-built for it.
- Crawl4AI on the tail too. Its p90 of 4754 ms is the best of the three; for high-volume jobs where consistency beats peak recall, that matters.
fastCRW's honest gaps are fixed and worth stating before you commit: no screenshot output (a formats: ["screenshot"] request returns HTTP 422), no multi-URL batched /v1/extract, no /v1/agent or /v1/deep-research, no Fire-engine anti-bot, no built-in residential proxy pool, and it is stateless per request. LLM extraction supports OpenAI and Anthropic providers only (the managed /v1/search answer path defaults to DeepSeek).
Choosing your web data API
Map the choice to the job, not to a feature checklist.
| Your job | What to optimize for | Lean toward |
|---|---|---|
| RAG corpus building | Recall + whole-site crawl | fastCRW (highest recall, /v1/crawl + /v1/map) |
| Live agent context | Search + scrape in one loop, low median latency | fastCRW search or a search-native API |
| Tail-latency-critical inline calls | Tight p90/p99 | Firecrawl or Crawl4AI |
| Hardened anti-bot targets | Residential proxies, stealth | A dedicated anti-bot vendor |
| Privacy / regulated data | Data never leaves your infra | fastCRW self-host |
| Single-URL markdown, occasional use | Simplicity | Jina Reader or fastCRW /v1/scrape |
For the output format itself — when markdown wins and when you should reach for JSON-schema extraction — see our walkthrough on LLM-ready markdown extraction. The short version: markdown for retrieval and chunking, JSON for typed fields you will query.
How to run a fair trial
Because fastCRW is Firecrawl-compatible, you do not have to decide on argument. Point the official Firecrawl SDK at a fastCRW base URL, run the same pipeline against both for a week on identical URLs, and capture four numbers identically: content-parity rate on a labeled sample, p50 and p90 latency, error mix, and projected monthly bill including any separate extraction subscription. Let the numbers arbitrate. If the tail matters more than recall for your traffic, the data will say so; if recall and median win, you have already migrated.
Sources
- fastCRW scrape benchmark of record:
bench/server-runs/RESULT_3WAY_1000_FULL.md(diagnose_3way.py, Firecrawl public dataset, 819 labeled URLs, 2026-05-08) - fastCRW canonical fact sheet: credit costs, API surface, structural footprint, honest gaps (
marketing/CANONICAL-FACTS.md§1, §3, §4, §5, §8, §9) - Competitor pricing:
marketing/competitor-prices.lock.md(verified 2026-05-18) · firecrawl.dev/pricing - fastCRW repo and pricing: github.com/us/crw · fastcrw.com
Related: Best web scraping APIs · LLM-ready markdown extraction · Best web scraping API 2026